The platform's core pieces: overall architecture, Delta Lake, and Unity Catalog
The Databricks Data Intelligence Platform combines a unified workspace for data engineering, analytics, and AI on top of open architecture, with Delta Lake as the storage foundation and Unity Catalog as the governance layer. It separates compute from storage and relies on cloud object storage as the single source of truth, avoiding data silos and duplicate copies. Understanding how the control plane, compute plane, Delta Lake, and Unity Catalog fit together is foundational to every other exam domain.
1 · Learn the must-know
- The platform architecture splits into a control plane (Databricks-managed: web app, notebooks, job scheduler, cluster manager) and a compute plane (runs in the customer's cloud account, executes actual data processing) so customer data stays within the customer's cloud environment.
- Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, and time travel to data stored as Parquet files in cloud object storage, forming the foundation of the lakehouse.
- Unity Catalog provides a unified governance layer across all workspaces in an account, using a three-level namespace (catalog.schema.table) to organize data and enabling fine-grained access control, auditing, lineage, and data discovery from one place.
- The lakehouse architecture combines the low-cost, flexible storage of a data lake with the ACID transactions and performance features of a data warehouse, eliminating the need to maintain separate systems for BI and ML/AI workloads.
- Compute resources (clusters, SQL warehouses) are ephemeral and can be started/stopped independently of storage, meaning data persists in cloud storage even when no compute is running.
- Unity Catalog's metastore is created at the account level and can be attached to multiple workspaces, so metadata, permissions, and governance policies are consistent across an entire organization rather than siloed per workspace.
2 · Check your understanding
A data engineering team at a financial services company must prove to auditors that raw customer records never leave the company's cloud account, even though Databricks fully manages notebooks, job scheduling, and cluster orchestration through a shared web application. Which description of the Databricks architecture confirms that this requirement is already satisfied?
What you have tried across Databricks DEA's objectives, not a readiness score.
Databricks Intelligence Platform6% of the exam0 of 2 tried
Data Ingestion and Loading21% of the exam0 of 7 tried
Data Transformation and Modeling22% of the exam0 of 7 tried
Working with Lakeflow Jobs16% of the exam0 of 4 tried
Implementing CI/CD10% of the exam0 of 4 tried
Troubleshooting, Monitoring, and Optimization10% of the exam0 of 5 tried
Governance and Security15% of the exam0 of 4 tried
3 · Keep going
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